113 citations · 202 across the 11 of their papers we have counts for
14 papers
High-performance deep spiking neural networks with 0.3 spikes per neuron
Ana Stanojevic, Stanisław Woźniak, Guillaume Bellec +3
Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks (S…
Factorizers for Distributed Sparse Block Codes
Michael Hersche, Aleksandar Terzic, Geethan Karunaratne +6
Distributed sparse block codes (SBCs) exhibit compact representations for encoding and manipulating symbolic data structures using fixed-width vectors. One major challenge however…
An Exact Mapping From ReLU Networks to Spiking Neural Networks
Ana Stanojevic, Stanisław Woźniak, Guillaume Bellec +3
Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks t…
On the visual analytic intelligence of neural networks
Stanisław Woźniak, Hlynur Jónsson, Giovanni Cherubini +2
Visual oddity task was conceived as a universal ethnic-independent analytic intelligence test for humans. Advancements in artificial intelligence led to important breakthroughs, ye…
In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory
Geethan Karunaratne, Michael Hersche, Jovin Langenegger +15
Continually learning new classes from a few training examples without forgetting previous old classes demands a flexible architecture with an inevitably growing portion of storage,…
Constrained Few-shot Class-incremental Learning
Michael Hersche, Geethan Karunaratne, Giovanni Cherubini +3
Continually learning new classes from fresh data without forgetting previous knowledge of old classes is a very challenging research problem. Moreover, it is imperative that such l…